Lasers are often used for welding as a non-contact energy source that requires minimum heat input. In particular, low power pulsed Nd:YAG lasers exhibiting high peak powers combined with small spot sizes can heat materials above their melting point, without significantly raising temperature next to the weld pool. Advanced features such as pulse shaping and laser power feedback have further improved solid-state lasers by providing increased control of the laser output. However, even with these advanced features, prediction of laser-material interaction is not straight forward; therefore, it is difficult to develop a reliable and easy-to-use laser weld monitoring system.
Conventional monitoring techniques use sensors such as infrared (IR), ultraviolet (UV), high-speed camera, sound, and transducer acoustic. However, it is often difficult to analyze the monitored weld characteristics to distinguish between good and bad welds because of the non-trivial nature of predicting laser-material interaction. Further, complex pattern matching and/or advanced mathematical techniques are often employed to analyze the profiles taken, further complicating the analysis and application process. Because of the complicated nature of the conventional monitoring techniques and difficulties associated with the analysis, users often do not understand the monitoring process, and instead rely entirely on the system developers to produce a system that will meet their process development and/or process control needs.
Therefore, it is desirable to provide a method and apparatus for capturing the weld characteristics, for analyzing the captured weld characteristics using simple mathematical algorithms, and for applying the analysis to distinguish between good and bad welds that are easier to understand and customizable to meet the process development and process control needs of each individual user.